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Avoiding the pain of out-of-specification results.(Company overview)

Publication: Biopharm International
Publication Date: 01-JUN-08
Format: Online
Delivery: Immediate Online Access

Article Excerpt
ABSTRACT

The pharmaceutical industry does not have a good track record of applying sound statistical principles to the investigation of out-of-specification (OOS) results. Recently, Steven Kuwahara presented an article on the history of the OOS problem highlighting some statistical (1) In...

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...deficiencies. this article, we present some additional statistical principles. Failure to apply these principles causes unnecessary investigations, nearly guarantees recurrence, and wastes valuable time and resources. Control charts can help scientists and managers avoid these pitfalls.

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[ILLUSTRATION OMITTED]

In many factories and laboratories, the prevalent mindset is that if the test result under examination remains within specifications, not only does nothing more need to be said about that result, but also that nothing more should be said about it. This fundamental error leads to statistically significant signals that ought to warn of impending trouble being ignored. In turn, these overlooked or disregarded signals and disturbances eventually result in out-of-specification (OOS) results that should have been avoided. They also lead to an unnecessary increase in variation that, in accordance with Little's Law, damages not only quality, but also productivity. (2) Any approach that blinds people to statistically valid signals and warnings is poor science. Control charts, which is a method of separating random from non-random variation, can help scientists and managers avoid these pitfalls.

PROCESS VARIATION

When a product batch is tested and is found to be out of specifications, the prevailing practice in the pharmaceutical industry is to study the faulty batch-independent of previous batches--to determine the cause of the OOS result. This can often lead to erroneous causal relationships, an incorrect determination of the root cause of the OOS result, and corrective action that almost guarantees future recurrences. The result is an increase in operational and compliance costs.

Driving this approach to OOS testing is the "go, no-go" mindset so prevalent in the industry. One real problem with the "go, no-go" mindset is that an OOS result must be recorded before any action is taken. Scientists tend to ignore the statistical signals.

The top chart in Figure 1 shows the batch...

NOTE: All illustrations and photos have been removed from this article.



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